Abstract
Meteorological Ensemble Streamflow Prediction (ESP), which uses Ensemble Weather forecasts (EWFs) to drive hydrological models, is a useful methodology for extending forecast periods and to provide valuable uncertainty information to improve the operation of future water resources. However, raw EWFs are usually biased and under-dispersive and so cannot be directly used in ESP, leading to the development of several post-processing methods. The performance of these methods needs to be evaluated/compared in building ESP based on deterministic and probabilistic criteria. In addition, likely influencing factors also need to be identified. This study evaluated the performance of four state-of-the-art methods: the Generator-based Post-Processing (GPP) method, Extended Logistic Regression (ExLR), Bayesian Model Averaging (BMA) and Affine Kernel Dressing (AKD), using a simple bias correction (BC) method as a benchmark. The evaluation was carried out over four watersheds with different basin areas in the humid region of central-south China based on the weather reforecasts from the Global Ensemble Forecasting System (GEFS). The results show that the performance of the post-processing methods varies with the forecast variable (precipitation, or air temperature or streamflow), but all of them outperform the BC and GEFS. For the four post-processing methods, the advantage of the generator-based methods (GPP and ExLR) lies in their probabilistic performance, which outperforms the distribution-based methods (BMA and AKD) by about 10% in precipitation forecasts and about 20% in streamflow forecasts, while the distribution-based methods (BMA and AKD) are better at their deterministic performance for precipitation forecasts, with a benefit of about 15%. Meanwhile, the post-processing methods generally perform better for precipitation and streamflow forecasts, but worse for air temperature forecasts for a bigger basin compared to the distribution-based methods. The results of this study emphasize the importance of considering the uncertainty of post-processing methods in ESP.
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Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (Grant No. 51779176, 51539009), the Overseas Expertise Introduction Project for Discipline Innovation (111 Project) funded by the Ministry of Education and State Administration of Foreign Experts Affairs P.R. China (Grant No. B18037), the Thousand Youth Talents Plan from the Organization Department of the CCP Central Committee (Wuhan University, China), and the Research Council of Norway (FRINATEK Project 274310). The authors would like to acknowledge the National Oceanic and Atmospheric Administration (NOAA), Boulder, Colorado, USA for providing GEFS ensemble precipitation and air temperature reforecasts. The authors wish to thank the China Meteorological Data Sharing Service System and the Hydrology and Water Resources Bureau of Hunan Province, China for providing the daily meteorological and hydrological data in the Xiangjiang basin.
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Li, XQ., Chen, J., Xu, CY. et al. Performance of Post-Processed Methods in Hydrological Predictions Evaluated by Deterministic and Probabilistic Criteria. Water Resour Manage 33, 3289–3302 (2019). https://doi.org/10.1007/s11269-019-02302-y
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DOI: https://doi.org/10.1007/s11269-019-02302-y